Several methods have been introduced to overcome these perceived short-comings. ADASYN [1]} adaptively selects minority examples for over-sampling based on learning difficulty and is generally implemented with binary classification. Remix [2]} aims to improve recall on minority classes by expanding the footprint of the minority class data. It is similar to EOS in that it augments the training data with convex combinations of samples from heterogeneous class. Remix, however, performs unsupervised augmentation and does so in the input space rather than the learned embedding space. As a result, the augmentations are expected to be more noisy and less targeted towards reducing the generalization gap than EOS. Like EOS and Remix, manifold-based oversampling [3]} aims to improve generalization by expanding the minority class footprint by generating samples along the underlying manifold of the target data. Similar to recent GAN-based oversampling techniques, however, manifold-based oversampling requires leaning an additional model for data generation.
